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Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Computers, Materials & Continua 2023年第7期76卷 1-17页

作者： mohammed salahat Liaqat Ali Taher M.Ghazal Haitham M.Alzoubi College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600Malaysia School of Business Skyline University CollegeUniversity City Sharjah1797SharjahUAE

Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

关键词： Personality traits convolutional neural network deep learning word embedding

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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

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Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh mohammed salahat Taher M.Ghazal Mohsen A.A.Tomh Beenu Mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

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Computers, Materials & Continua 2024年第4期79卷 875-907页

作者： mohammed Alatiyyah Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.

关键词： Public transportation digital environment passenger estimation signal capturing Wi-Fi

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Human dental pulp stem/stromal cells in clinical practice

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World Journal of Stem Cells 2024年第2期16卷 54-57页

作者： mohammed E Grawish Department of Oral Biology Faculty of DentistryMansoura UniversityMansoura 35561Egypt Department of Oral Biology Faculty of Oral and Dental MedicineDelta University for Science and TechnologyMansoura 11152Egypt

Dental pulp stem/stromal cells(DPSCs)are fibroblast-like,neural crest-derived,and multipotent cells that can differentiate into several lineages.They are relatively easy to isolate from healthy and inflamed pulps,with little ethical concerns and can be successfully cryopreserved and thawed.The therapeutic effects of DPSCs derived from animal or human sources have been extensively studied through in-vitro and in-vivo animal experiments and the findings indicated that DPSCs are effective not only for dental diseases but also for systemic diseases.Understanding that translational research is a critical step through which the fundamental scientific discoveries could be translated into applicable diagnostics and therapeutics that directly benefit humans,several clinical studies were carried out to generate evidence for the efficacy and safety of autogenous or allogeneic human DPSCs(hDPSCs)as a treatment modality for use in cell-based therapy,regenerative medicine/dentistry and tissue engineering.In clinical medicine,hDPSCs were effective for treating acute ischemic stroke and human exfoliated deciduous teeth-conditioned medium(SHED-CM)repaired vascular damage of the corpus cavernous,which is the main cause of erectile dysfunction.Whereas in clinical dentistry,autologous SHED was able to rege-nerate necrotic dental pulp after implantation into injured teeth,and micrografts enriched with autologous hDPSCs and collagen sponge were considered a treatment option for human intrabony defects.In contrast,hDPSCs did not add a significant regenerative effect when they were used for the treatment of post-extraction sockets.Large-scale clinical studies across diverse populations are still lacking to provide robust evidence on the safety and efficacy of hDPSCs as a new treatment option for various human diseases including dental-related problems.

关键词： Dental pulp stem/stromal cells Human clinical studies Regenerative medicine Regenerative dentistry Cell-based therapy

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Kaoline-based catalyst for a high stability desulfurization of sour heavy naphtha in a three-phase oscillatory baffled reactor

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Particuology 2024年第1期84卷 249-260页

作者： Hassan M.Hmood Saba A.Gheni Safaa M.R.Ahmed Mudheher M.Ali Hamid Yonis Saleh mohammed H.mohammed Awad E.mohammed Marwan A.Mahomood Hiba R.mohammed Ahmed A.Hassan Adam Harvey North Refineries Company Ministry of OilIraq Chemical Engineering Department Tikrit UniversityIraq Ministry of Oil Iraq AL-Hawija Technical Institute Northern Technical UniversityMoselIraq School of Engineering Newcastle UniversityUK

To meet the rapidly increasing demand for energy and the dramatic depletion of conventional crude oil,it is imperative to utilize sour naphtha.With no coke being produced,oxidative desulfurization(ODS)of naphtha lowers its sulfur content and average molecular weight.In this article,we outline a method for heavy naphtha non-extractive ODS using a very stable catalyst.The technique involves the use of a solid catalyst with oxygen gas as the oxidant.This necessitated relatively high mixing intensities;hence a three-phase Oscillatory Baffled Reactor(OBR)was used.The catalyst was based on the zeolite ZSM-5,prepared from natural kaolin by a series of delamination and activation steps and impregnated with Fe.A TiO2 nanolayer was applied,using the sol-gel method,to prevent rapid deactivation.The reactor performance was evaluated to minimize the sulfur content in the naphtha fuel.Due to the protective coating,the sulfur conversion stabilized at 90%.The results of this work establish the use of natural clay-based catalysts in a continuous,three-phase ODS,particularly with regard to proving long-term stability.It also showed that modest ODS can be achieved using an environmentally friendly oxidant,at mild operating conditions,whilst maintaining stability.

关键词： Natural kaoline Desulfurization Three-phase Oscillatory baffled reactor

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MoBShield:A Novel XML Approach for Securing Mobile Banking

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Computers, Materials & Continua 2024年第5期79卷 2123-2149页

作者： Saeed Seraj Ali Safaa Sadiq OmprakashKaiwartya Mohammad Aljaidi Alexandros Konios mohammed Ali mohammed Abazeed Department of Computer Science Nottingham Trent UniversityClifton LaneNottinghamNG118NSUK Department of Computer Science Faculty of Information TechnologyZarqa UniversityZarqa13110Jordan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness to society.However,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking information.Related literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security functionality.In this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets.The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission requests.Further,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour.Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives.Specifically,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art approaches.These promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking applications.Consequently,MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms,mitigating the risks p

关键词： Security malware detection deep learning convolutional neural networks deep neural networks

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An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 421-426页

作者： Weihua Zhou Bin Liu Nabil mohammed Behrooz Bahrani Department of Electrical and Computer Systems Engineering Monash UniversityMelbourne 3800Australia Energy Centre Commonwealth Scientific and Industrial Research Organisation(CSIRO)Newcastle 2300Australia.He is now with the Network Planning DivisionTransgridSydney 2000Australia

Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary nature.Admittance measurements,relying on electromagnetic transient simulation or laboratory settings,are not only time-intensive but also operationally inflexible,since various non-linear control loops make IBRs’admittance models operating-point dependent.Therefore,such admittance measurements must be performed repeatedly when operating point changes.To avoid time-consuming and cumbersome measurements,admittance estimation for arbitrary operating points is highly desirable.However,existing admittance estimation algorithms usually face challenges in versatility,data demands,and accuracy.Addressing this challenge,this letter presents a simple and efficient admittance estimation method for blackboxed IBRs,by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system.Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs.Estimation accuracy is satisfying even when non-negligible measurement errors exist.

关键词： Admittance estimation black box inverterbased resource measurement error operating point

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Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1893-1932页

作者： Nadeem Salamat Muhammad Kamran Shahzaib Ashraf Manal Elzain mohammed Abdulla Rashad Ismail mohammed M.Al-Shamiri Institute of Mathematics Khwaja Fareed University of Engineering&Information TechnologyRahim Yar Khan64200Pakistan Department of Mathematics Thal University BhakkarPunjab30000Pakistan Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl Assir61913Saudi Arabia

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in

关键词： Intuitionistic fuzzy set quaternion numbers fuzzy logic decision-making rough set

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Dynamic thermal performance of multilayer hollow clay walls filled with insulation materials:Toward energy saving in hot climates

Energy and Built Environment

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Energy and Built Environment 2024年第1期5卷 70-80页

作者： Yassine Chihab mohammed Garoum Najma Laaroussi mohammed V University in Rabat MaterialsEnergy and Acoustics TEAMRabatMorocco

This study examines the benefits of incorporating passive techniques into multilayer hollow clay brick walls to improve their dynamic thermal performance.The finite element approach was used to solve the incompressible Navier-Stokes and energy equations to analyze the dynamic thermal response of walls exposed to real thermal excitations of the Marrakesh climate.The results show that increasing the emissivity from 0.1 to 0.9 significantly increases the total heat load over 24 h.Furthermore,filling 100% of the cavities with insulation materials delayed the temperature peak by about 2.3 h and lowered the decrement factor by roughly 43%,with a value smaller than 0.07.In addition,it is demonstrated that the total thermal load is reduced by approximately 28% for improved wall configurations(100% insulation filling cavities)compared to traditional wall configurations(100% air filling cavities),which aids in improving building energy efficiency.

关键词： Time lag Decrement factor Coupled heat transfer Analytical solution Hollow clay brick Insulation filling cavities Energy savings in building

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Secure android IoT mobile and collaborative machine learning for controlling the management of enterprise

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Journal of Control and Decision 2024年第1期11卷 15-25页

作者： Hamza mohammed Ridha Al-Khafaji Refed Adnan Jaleel Biomedical Engineering Department Al-Mustaqbal University CollegeHillahIraq Information and Communication Engineering Department Al-Nahrain UniversityBaghdadIraq

There is a need for increased security measures because of wide variety of android Internet of Thing(IoT)mobile devices that can communicate with each other via networks for controlling the management of enterprise.Elliptic Curve Deffie Hellman(ECDH)and Rivest Shamir Adleman(RSA)are used to secure data in android IoT phones in efficient manner.Android mobile can store a lot of data,including sensitive data.Protecting data saved on mobile has become a critical problem.In android IoT,Collaborative Machine Learning describes a method for collaboratively mining data,which makes it easier to manage and lowers cost of maintenance.To increase security in IoT phones,suggested system uses ECDH,RSA,and CML algorithms,which have been considered novelty of this method.RSA and ECDH are computed using time of decryption,encryption,and key generation.Conclusions show ECDH beats other alternatives like RSA.Finally,all users of the network have been tested.

关键词： IoT ECDH RSA CML android encryption decryption

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